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首页> 外文期刊>Journal of applied geodesy >Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine
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Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine

机译：通过与最小二乘支持向量机集成来增强最小二乘配置的可预测性

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Least-squares collocation (LSC) is a crucial mathematical tool for solving many geodetic problems. It has the capability to adjust, filter, and predict unknown quantities that affect many geodetic applications. Hence, this study aims to enhance the predictability property of LSC through applying soft computing techniques in the stage of describing the covariance function. Soft computing techniques include the support vector machine (SVM), least-squares-support vector machine (LS-SVM), and artificial neural network (ANN). A real geodetic case study is used to predict a national geoid from the EGM2008 global geoid model in Egypt. A comparison study between parametric and soft computing techniques was performed to assess the LSC predictability accuracy. We found that the predictability accuracy increased when using soft computing techniques in the range of 10.2%-27.7% and 8.2%-29.8% based on the mean square error and the mean error terms, respectively, compared with the parametric models. The LS-SVM achieved the highest accuracy among the soft computing techniques. In addition, we found that the integration between the LS-SVM with LSC exhibits an accuracy of 20 % and 25 % higher than using LS-SVM independently as a predicting tool, based on the mean square error and mean error terms, respectively. Consequently, the LS-SVM integrated with LSC is recommended for enhanced predictability in geodetic applications.
机译：最小二乘搭配（LSC）是解决许多大地测量问题的重要数学工具。它具有调整，过滤和预测影响许多大地测量应用程序的未知量的功能。因此，本研究旨在通过在描述协方差函数的阶段应用软计算技术来增强LSC的可预测性。软计算技术包括支持向量机（SVM），最小二乘支持向量机（LS-SVM）和人工神经网络（ANN）。一个真实的大地测量案例研究被用来根据埃及的EGM2008全球大地水准面模型预测国家大地水准面。在参数和软计算技术之间进行了比较研究，以评估LSC的可预测性准确性。我们发现，与参数模型相比，基于均方误差和均误差项，使用软计算技术时，可预测性准确性分别提高了10.2％-27.7％和8.2％-29.8％。 LS-SVM在软计算技术中达到了最高的精度。此外，我们发现，分别基于均方误差和均误差项，与单独使用LS-SVM作为预测工具相比，LS-SVM与LSC的集成显示出20％和25％的精度。因此，建议使用与LSC集成的LS-SVM以提高大地测量应用程序的可预测性。

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